Pathogen Biology Laboratory, Department of Biotechnology and Bioinformatics, University of Hyderabad, Hyderabad, Telangana, India.
Pathogen Biology Laboratory, Department of Biotechnology and Bioinformatics, University of Hyderabad, Hyderabad, Telangana, India.
Infect Genet Evol. 2024 Oct;124:105666. doi: 10.1016/j.meegid.2024.105666. Epub 2024 Sep 4.
The human gut presents a complex ecosystem harboring trillions of microorganisms living in close association with each other and the host body. Any perturbation or imbalance of the normal gut microbiota may prove detrimental to human health. Enteric infections and treatment with antibiotics pose major threats to gut microbiota health. Recent genomics-driven research has provided insights into the transmission and evolutionary dynamics of major enteric pathogens such as Escherichia coli, Klebsiella pneumoniae, Vibrio cholerae, Helicobacter pylori and Salmonella spp. Studies entailing the identification of various dominant lineages of some of these organisms based on artificial intelligence and machine learning point to the possibility of a system for prediction of antimicrobial resistance (AMR) as some lineages have a higher propensity to acquire virulence and fitness advantages. This is pertinent in the light of emerging AMR being one of the immediate threats posed by pathogenic bacteria in the form of a multi-layered fitness manifesting as phenotypic drug resistance at the level of clinics and field settings. To develop a holistic or systems-level understanding of such devastating traits, present methodologies need to be advanced with the high throughput techniques integrating community and ecosystem/niche level data across different omics platforms. The next major challenge for public health epidemiologists is understanding the interactions and functioning of these pathogens at the community level, both in the gut and outside. This would provide new insights into the dimensions of enteric bacteria in different environments and niches and would have a plausible impact on infection control strategies in terms of tackling AMR. Hence, the aim of this review is to discuss virulence and AMR in Gram-negative pathogens, the spillover of AMR and methodological advancements aimed at addressing it through a unified One Health framework applicable to the farms, the environment, different clinical settings and the human gut.
人类肠道呈现出一个复杂的生态系统，其中栖息着数以万亿计的微生物，它们彼此之间以及与宿主身体密切相关。任何对正常肠道微生物群的干扰或失衡都可能对人类健康造成不利影响。肠道感染和抗生素治疗对肠道微生物群的健康构成了重大威胁。最近的基因组学驱动的研究为大肠杆菌、肺炎克雷伯菌、霍乱弧菌、幽门螺杆菌和沙门氏菌等主要肠道病原体的传播和进化动态提供了深入了解。基于人工智能和机器学习对这些生物体的各种优势谱系进行鉴定的研究表明，有可能建立一个预测抗生素耐药性（AMR）的系统，因为一些谱系更有可能获得毒力和适应性优势。鉴于新兴的 AMR 是致病菌以表型药物耐药性的形式在临床和现场环境层面表现出的多层次适应性的一种即时威胁，这一点尤为重要。为了全面或系统地了解这些破坏性特征，需要利用整合不同组学平台上社区和生态系统/小生境水平数据的高通量技术来推进现有的方法。公共卫生流行病学家面临的下一个主要挑战是了解这些病原体在社区层面，包括在肠道内外的相互作用和功能。这将为不同环境和小生境中肠道细菌的维度提供新的见解，并在应对 AMR 方面对感染控制策略产生合理的影响。因此，本综述的目的是讨论革兰氏阴性病原体的毒力和 AMR、AMR 的溢出以及通过适用于农场、环境、不同临床环境和人类肠道的统一的“One Health”框架来解决它的方法学进展。
